How do you equip your logistics, supply chain, and mobility teams with AI capabilities before the competition does?

A dispatch team needs a mix of technical fundamentals, process integration and change management. First, we teach the basics of how models make decisions: inputs, outputs, uncertainty measures and typical failure modes. Understanding this mechanics reduces mistrust and enables more deliberate decisions.

In the second step, the workshops focus on integration: how are copilot suggestions embedded into existing TMS workflows, which data feeds must be checked (e.g. order priority, vehicle status, traffic data) and what do escalation paths look like when suggestions are not practicable. Practical exercises with real cases ensure the team does not just observe but actively interacts.

A third component is prompting and interaction design: we train dispatchers how to query the copilot correctly, request alternative scenarios and document human interventions. These skills increase the quality of human-AI collaboration and ease subsequent monitoring.

Finally, we offer on-the-job coaching where experts are present for multiple shifts to answer questions in real time and make adjustments. This establishes the tool as a reliable partner in daily operations rather than a black box.

Success is measured by operational KPIs, behavior changes and the sustainability of usage. Operational metrics can include: improved forecast accuracy, reduced planning time per run, reduction of empty miles or increased on-time delivery rate. These KPIs should be defined before training and measured regularly.

In parallel we capture usage metrics of the AI tools: How often do dispatchers accept copilot suggestions? What adjustments are made? A high interaction rate with a decreasing manual correction rate is a good signal of adoption.

Qualitative measurement is also essential: user feedback, error reports and case studies from daily business. These insights show whether training content was understood and whether playbooks work in critical situations.

In the long term, organizational indicators also matter: new role appointments (e.g. AI Product Owner), participation in internal communities of practice and scaling pilots to other regions or business units.

The transition to e-mobility changes the decision basis in fleet operations: charging time windows, range variability, state-of-charge management and charging infrastructure constraints become central planning parameters. Our trainings first convey the physical and operational fundamentals of these new dimensions so managers understand why traditional KPIs must be rethought.

Technically, we work with simulations and scenarios: How do different charging profiles affect route planning and availability? What reserves must be planned? Through practical exercises fleet managers learn to integrate charging windows into dispatch and evaluate trade-offs between range, charging costs and service level.

Another focus is data integration: telemetry data, charging infrastructure APIs and battery data must be correctly interpreted. We show what data quality is required, how to detect anomalies and how to maintain predictive models so decisions remain robust.

Finally, we support tool rollouts on the job and create playbooks for operations under varying grid loads and seasonal effects. This builds operational resilience rather than mere technology adoption.

Governance is not an add-on but a core element: logistics companies operate in a regulated environment with high SLAs and often sensitive user data (drivers, delivery recipients). Governance ensures that AI-supported decisions are traceable, auditable and compliant. In our programs, governance is integrated from the start.

We cover topics such as data ownership, access controls, logging of model decisions and criteria for human overriding. Practical artifacts include audit logs, decision playbooks and clear metrics for model monitoring that are developed and tested in the trainings.

Another aspect is ethical AI: bias risks in automatic prioritization of orders or in driver evaluations must be identified and mitigated. We train teams to spot warning signals and define escalation flows for potentially discriminatory decisions.

Finally, we translate governance requirements into manageable processes so teams are not faced with abstract principles but with concrete checklists and responsibilities they can use daily.

Non-technical users need tools and frameworks that hide complexity without sacrificing transparency. Our AI Builder Track is precisely designed for this: we teach low-code methods, standardized prompting frameworks and modular playbooks that allow power users to develop their own automations and workflows.

The emphasis is on reuse: instead of retraining a model every time, we teach how to parameterize existing copilots, refine prompts and configure simple data pipelines. This creates quickly usable prototypes that can be integrated into daily operations.

In parallel we provide governance guards: templates for data access, validation checks and review processes so non-technical users do not unintentionally introduce risks. The combination of empowerment and protection is crucial for sustainable adoption.

Finally, we ensure successful approaches are shared and evolved through internal communities of practice — creating a continuous learning and innovation network within the organization.

Time to measurable results varies depending on scope and starting maturity: For focused pilots, such as a planning copilot for a regional corridor, we often see quick wins within 2–6 weeks: improved dispatch suggestions, reduced planning time and initial KPI improvements.

For broad rollouts, including governance, playbooks and on-the-job coaching across multiple departments, we expect 3–6 months until substantial, noticeable improvements in forecast accuracy, fleet utilization and cost efficiency. This timeframe also includes important steps like data cleansing, user adoption and iterative model tuning.

It is important that results are documented both quantitatively and qualitatively: operational metrics plus user feedback. This allows informed scaling decisions.

Our co-preneur mentality means we remain operationally involved until the organization owns the new capabilities — this reduces time-to-value and increases the sustainability of the measures.

A management briefing focuses on strategic implications, risk assessment and value potential. First, we clarify concrete business objectives: cost reduction, service improvement, emissions targets or scaling e-mobility fleets. These goals drive the selection of the right AI initiatives.

We then present a pragmatic roadmap: prioritized use cases, investment estimates, necessary data and infrastructure measures and expected KPIs. It is important to present verifiable quick experiments alongside long-term architecture decisions.

Another component is governance and compliance: we outline which regulatory questions are relevant, how data protection is handled and what internal organization is needed to manage risk. C-level needs concrete decision options here, not technical minutiae.

Finally, we provide recommendations for organizational design: which roles should be built internally, how to involve partners and which KPIs should be used as steering variables. The management briefing therefore delivers not only orientation but also concrete action options and decision scenarios.